ABSTRACT
Fairness and related concerns have become of increasing importance in a variety of AI and machine learning contexts. They are also highly relevant to information retrieval and related problems such as recommendation, as evidenced by the growing literature in SIGIR, FAT*, RecSys, and special sessions such as the FATREC workshop and the Fairness track at TREC 2019; however, translating algorithmic fairness constructs from classification, scoring, and even many ranking settings into information retrieval and recommendation scenarios is not a straightforward task. This tutorial will help to orient IR researchers to algorithmic fairness, understand how concepts do and do not translate from other settings, and provide an introduction to the growing literature on this topic.
- N. J. Belkin and S. E. Robertson. 1976. Some ethical and political implications of theoretical research in information science. In Proceedings of the ASIS Annual Meeting.Google Scholar
- Alex Beutel, Jilin Chen, Tulsee Doshi, Hai Qian, Allison Woodruff, Christine Luu, Pierre Kreitmann, Jonathan Bischof, and Ed H. Chi. 2019. Putting Fairness Principles into Practice: Challenges, Metrics, and Improvements. CoRR, Vol. abs/1901.04562 (2019).Google Scholar
- Asia J Biega, Krishna P Gummadi, and Gerhard Weikum. 2018. Equity of Attention: Amortizing Individual Fairness in Rankings. In Proc. SIGIR '18. ACM, 405--414. Google ScholarDigital Library
- Robin Burke. 2017. Multisided Fairness for Recommendation. (July 2017). arxiv: cs.CY/1707.00093 http://arxiv.org/abs/1707.00093Google Scholar
- Alexandra Chouldechova and Aaron Roth. 2018. The Frontiers of Fairness in Machine Learning. (Oct. 2018). arxiv: cs.LG/1810.08810 http://arxiv.org/abs/1810.08810Google Scholar
- Fernando Diaz. 2016. Worst Practices for Designing Production Information Access Systems. SIGIR Forum, Vol. 50, 1 (June 2016), 2--11. Google ScholarDigital Library
- Michael D Ekstrand and Amit Sharma. 2017. FATREC Workshop on Responsible Recommendation. In Proc. ACM RecSys '18. ACM, 382--383. Google ScholarDigital Library
- Michael D Ekstrand, Mucun Tian, Ion Madrazo Azpiazu, Jennifer D Ekstrand, Oghenemaro Anuyah, David McNeill, Pera, and Maria Soledad. 2018a. All The Cool Kids, How Do They Fit In?: Popularity and Demographic Biases in Recommender Evaluation and Effectiveness. In Proceedings of the Conference on Fairness, Accountability, and Transparency (PMLR), Vol. 81. 172--186. http://proceedings.mlr.press/v81/ekstrand18b.htmlGoogle Scholar
- Michael D Ekstrand, Mucun Tian, Mohammed R Imran Kazi, Hoda Mehrpouyan, and Daniel Kluver. 2018b. Exploring Author Gender in Book Rating and Recommendation. In Proc. ACM RecSys '18. ACM. Google ScholarDigital Library
- Kenneth Holstein, Jennifer Wortman Vaughan, Hal Daumé III, Miro Dud'ik, and Hanna Wallach. 2019. Improving fairness in machine learning systems: What do industry practitioners need?. In Proc. CHI 2019. Google ScholarDigital Library
- Toshihiro Kamishima, Pierre-Nicolas Schwab, and Michael D Ekstrand. 2018. 2nd FATREC workshop: responsible recommendation. In Proc. ACM RecSys '18. ACM, 516--516. Google ScholarDigital Library
- Rishabh Mehrotra, Ashton Anderson, Fernando Diaz, Amit Sharma, Hanna Wallach, and Emine Yilmaz. 2017. Auditing Search Engines for Differential Satisfaction Across Demographics. In WWW '17 Companion. International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, Switzerland, 626--633. Google ScholarDigital Library
- Rishabh Mehrotra, James McInerney, Hugues Bouchard, Mounia Lalmas, and Fernando Diaz. 2018. Towards a Fair Marketplace: Counterfactual Evaluation of the trade-off between Relevance, Fairness and Satisfaction in Recommendation Systems. In Proc. CIKM '18. Google ScholarDigital Library
- Safiya Umoja Noble. 2018. Algorithms of Oppression: How Search Engines Reinforce Racism. NYU Press.Google Scholar
- Ashudeep Singh and Thorsten Joachims. 2018. Fairness of Exposure in Rankings. In Proc. KDD '18 (KDD '18). ACM, New York, NY, USA, 2219--2228. Google ScholarDigital Library
Index Terms
- Fairness and Discrimination in Retrieval and Recommendation
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